TF之NN:利用神经网络系统自动学习散点(二次函数+noise+优化修正)输出结果可视化(matplotlib动态演示)
目录
- import tensorflow as tf
- import numpy as np
- import matplotlib.pyplot as plt
-
- def add_layer(inputs, in_size, out_size, activation_function=None):
- Weights = tf.Variable(tf.random_normal([in_size, out_size]))
- biases = tf.Variable(tf.zeros([1, out_size]) + 0.1)
- Wx_plus_b = tf.matmul(inputs, Weights) + biases
- if activation_function is None:
- outputs = Wx_plus_b
- else:
- outputs = activation_function(Wx_plus_b)
- return outputs
-
- x_data = np.linspace(-1,1,300)[:, np.newaxis]
- noise = np.random.normal(0, 0.05, x_data.shape)
- y_data = np.square(x_data) - 0.5 + noise
-
- define placeholder for inputs to network
- xs = tf.placeholder(tf.float32, [None, 1])
- ys = tf.placeholder(tf.float32, [None, 1])
-
- l1 = add_layer(xs, 1, 10, activation_function=tf.nn.relu)
-
- prediction = add_layer(l1, 10, 1, activation_function=None)
-
- the error between prediciton and real data
- loss = tf.reduce_mean(
- tf.reduce_sum(tf.square(ys - prediction),reduction_indices=[1])
- )
- train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)
- important step
- init = tf.global_variables_initializer()
- sess = tf.Session()
- sess.run(init)
-
- plot the real data
- fig = plt.figure()
- ax = fig.add_subplot(1,1,1)
- ax.scatter(x_data, y_data)
- plt.ion()
- plt.show()
-
- for i in range(1000):
- training
- sess.run(train_step, feed_dict={xs: x_data, ys: y_data})
- if i % 50 == 0:
- to visualize the result and improvement
- try:
- ax.lines.remove(lines[0])
- except Exception:
- pass
- prediction_value = sess.run(prediction, feed_dict={xs: x_data})
- plot the prediction
- lines = ax.plot(x_data, prediction_value, 'r-', lw=5)
- plt.title('Matplotlib,NN,Efficient learning,Approach,Quadratic function --Jason Niu')
- plt.pause(0.1)
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TF之NN:matplotlib动态演示深度学习之tensorflow将神经网络系统自动学习散点(二次函数+noise)并优化修正并且将输出结果可视化
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